Abstract: The research technique of human motor nerve is more complicated. In order to improve the understanding level of human motor nerve structure, a fusion architecture of human motion nerve and neural network computer driven by education are proposed. The human motor nerve simulation model is established by using the computer data simulation model. The model proposes an optimization scheme from the algorithm flow, the data transformation technology used to improve the real-time performance of the neural network and enhance the dynamic capture of the motor nerve changes. In order to further improve the architecture of the neural network and computer architecture driven by sports, a neural network algorithm is added to realize the sequence optimization of data to test the authenticity and efficiency of the human neural network simulation model neural network algorithm. In this paper, the feasibility and functionality of the algorithm are tested with comparative experiments. The results show that the neural network algorithm neural network algorithm not only has a calculation time of only 12 seconds. Moreover, the calculation accuracy is high, achieving a high level of accuracy of 98%. On the other hand, other algorithms have lower accuracy and longer calculation time. The research shows that the neural network algorithm can improve the human motor nerve capture and optimize the architecture, which can provide reference for the fusion of human motion nerve and computer technology in the future.
